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Found 5 papers, 1 papers with code
Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxies
To address this problem, we propose a novel sampling distribution based on nonparametric kernel regression that learns an effective importance score as the neural network trains.
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More
Our work highlights several novel perspectives and opportunities for implementing randomized algorithms for deep learning on modern CPUs.
A Truly Constant-time Distribution-aware Negative Sampling
Softmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval.
A Tale of Two Efficient and Informative Negative Sampling Distributions
Unfortunately, due to the dynamically updated parameters and data samples, there is no sampling scheme that is provably adaptive and samples the negative classes efficiently.
SDM-Net: A Simple and Effective Model for Generalized Zero-Shot Learning
Zero-Shot Learning (ZSL) is a classification task where we do not have even a single training labeled example from a set of unseen classes.
